gymbeaux

It’s easier to open an app on my phone, and I believe wholeheartedly that Robinhood works hard to make their UI/UX as “fun” (dopamine-releasing) as possible. One area where Robinhood would be easier than any web app is biometric authentication. Not even having to sign in to the app makes it that much faster and easier to get a dopamine hit from placing a trade.

Robinhood makes their app fun to use with things like swiping up to place the trade, sometimes they put confetti on the screen… it really has quite a lot in common with an actual phone game like Monopoly Go or Candy Crush.

Both are console-sized x86 based computers that can run Windows or Linux. Both even lack a 3.5mm headphone jack (maybe that’s too old for a 2026 computer though). Both have a low TDP that means they will never compete with a traditional gaming computer.

I’m not sure what Proton has to do with anything.

If you have an options strategy that works 99% of the time, then you’re a millionaire, and if you’re a millionaire I have to ask what you’re doing here replying to people about options trading.

I mean unfortunately, as with the rest of the constitution, it’s up to the courts (ultimately the Supreme Court) to interpret, and the majority will never condone revolution, no matter how corrupt or ineffective the government becomes.

I think not many people realize that if the American Revolution was unsuccessful, the founding fathers would have been hanged for treason. It’s nice to have that given to us as a “right”, but with no means to enforce that right it may as well not exist at all. As time goes on I become more and more convinced that the founding fathers were exactly like Elon Musk- greedy, selfish, and out to brainwash the populace into doing their bidding. Many were wealthy slave owners, after all.

Most brokerages don’t have an app that encourages reckless trading by exploiting dopamine. This becomes that age-old argument about where the corporation’s responsibility ends and the individual’s responsibility begins. I guarantee it’s a lot harder to blow your retirement on 0DTE options via a “traditional” broker than an “app first” brokerage like Robinhood.

It’s harder to do on Fidelity. Robinhood plays on human psychology to make their money.

The plan is simply for insiders to offload their bags (overvalued shares) to retail investors, like the folk who frequent the wallstreetbets subreddit and do all their “investing” on Robinhood. This speaks to the larger trend of the lower and middle class apparently giving up on any plans to retire- and it’s understandable as the federal minimum wage has never been so disconnected from the cost of living, plus inflation, plus the current job market being universally terrible (even for software engineers), et al.

It’s worth noting that this is nothing new. I’m reminded of Virgin Galactic, another “space company” that was heavily speculated on. Predictably, insiders like Richard Branson himself sold a large number of shares (well into the millions of dollars) ahead of the inevitable “dump” where the share price fell around 75% in a matter of weeks. Virgin Galactic (SPCE) entered the Nasdaq via SPAC - Special Purpose Acquisition Group - essentially a company whose sole purpose is to acquire a private company, thereby effectively making the company public. Why wouldn’t Virgin Galactic just go public? Why go through a SPAC? The short answer is “to bypass regulations.”

OpenInsider is an excellent website that makes it easy to see when insiders buy or sell a stock, and the most common pattern is insiders dumping their shares in overvalued companies. We saw it when Zoom and Crispr and a few others shot up several hundred percentage points during COVID. C-suite and board members made out like bandits. Those weren’t even SPACs, those were just companies that people were foolish enough to speculate on.

Finally I want to bring attention to Robinhood, the stock trading platform that eliminated commission on trades from all brokerages - Schwab, Fidelity, Merrill Lynch, et al- by making it incredibly quick and easy (and free) to buy and sell stocks. They opened this Pandora’s Box, though I suppose it was bound to happen eventually- brokerages charged $7 per trade (sometimes more) and obviously for the college student who wants to throw $20 at Amazon stock… losing $7 in and then $7 again on the way out makes no sense. Now for anyone giving Robinhood the benefit of the doubt- their evil was (I think) absolutely confirmed when they unveiled a new feature- you can now trade OPTIONS with your retirement account. Options are essentially gambling, so to enable people to throw away their retirement on gambling is truly vile.

The Steam Machine makes sense if it costs the same or less than a current-gen console, but a whole grand for this feels icky. I paid around $300 for the original Steam Machine (Alienware Alpha) in 2014. It played Fallout 4 better than the PS4 and Xbox One which cost about the same. The "tradeoff" was that you had to maintain the PC's OS, which at the time had to be Windows 10.

The PS5 base has 2 more physical cores (4 more threads) than this Steam Machine so I'm not optimistic that the Steam Machine outperforms the PS5.

(both "semicustom AMD" so probably effectively the same architecture)

Circa 2013 you could get a Steam Machine (made by Alienware/Dell) - the Alienware Alpha - for something like $300. Granted they were clearing them out at that time, but $300 was a no-brainer when consoles cost about the same and had significantly weaker hardware.

Now we're expected to pay almost 2x the cost of a current-gen console for what is probably near-identical console performance? Doesn't make sense. I appreciate Valve being in the hardware business and I understand that inflation/the AI bubble are hurting PC components but a grand for this is a terrible value. I mean let's see what the benchmarks look like, but "Semi-custom AMD Zen 4 6C / 12T" sounds like what's in the PS5 and Xbox One. Actually those have 8C/16T CPUs.

I recognize that I seem to be an above-average developer and so my standards for LLM output are higher. I'm seeing enough comments where people are comparing LLM output to offshore dev output or even onshore mid-level or junior-level output. I guess my response would be something like "why are we celebrating this as a huge success for mankind?" If we dump obscene amounts of electricity and silicon into datacenters, we can produce mediocre or "adequate" software engineers?

I agree that LLM output tends to be absolutely flooded with comments. I think it helps to quickly see "is the output what I expected, does it do everything it should be doing in this method?" but I also think most people don't bother reading the comments.

What I'm hearing is "thoroughly reviewing AI generated code would defeat the purpose, so we give it a cursory glance and it seems to be decent code", and that's my point - it does indeed seem to be decent code but I think we're all kicking the can down the road when we operate this way. If the alternative means there's no gain to be had by using LLMs to write code, so be it. Maybe that's the answer. Maybe we shouldn't be relying so much on AI to write our code.

I think LLMs are great for writing small snippets of code that really only have one "best answer" (something simple like writing an array to a CSV), and internal tools, where bugs and security vulnerabilities usually aren't a big deal.

True. I used to get code that wouldn't compile all the time from Infosys "developers" in India circa 2016. Perhaps now with LLMs they still do basically no work, but at least the code compiles? That being said, I'm not sure that paying for Opus/GPT/Gemini makes sense for a company like Infosys that caters to the dumb C-Levels of large corporations who think "why pay X for U.S. devs when we can pay X/5 for foreign devs?" - such companies are fucked in the long-term anyway. Why would Infosys voluntarily pay gobs of money when such U.S. corporations seem to be content with their output as-is?

If I have to verify the code then I don't see a point in using it to write more than a single method at a time, and that method should be simple enough that I can take a very quick glance and be able to tell it's correct - something like a method that writes an array to a CSV. I don't have that code memorized, especially in the various languages I regularly work in, but I know it when I see it. Anything more complex than that and I think it would take me as much or more time to truly verify the AI's output than to just write it myself.

Yes, thank you for wording it better. When I read through for example an entire codebase that was ~99% written by AI, it's "inconsistent" in a way that even a shared-by-humans codebase would not be. I think this arises from the AI misunderstanding slightly what is being asked - the AI misunderstands, but can still (at least in some cases) output code that does what it needs to. It may also do other things that it doesn't need to do, or may do the thing in a suboptimal, not-so-maintainable way, but the UI works and that's enough for most non-technical people.

Those numbers are abysmal. Should we really be using LLMs to write our code? I have a theory- LLMs can spit out code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time. An enterprise app developed entirely with LLM-happy devs might end up virtually unmaintainable.

I’m not sure how to explain it, but the more I see LLM-written code the more I feel it’s bad code doing a good job of masquerading as good code. I think this take will become less-hot in the next year or two when we see enterprise greenfield projects that were created entirely with LLM “assistance” go to prod. I think we’ll find that the code is difficult for humans to read, understand, debug, and extend- and I think the larger the codebase the harder it will be for LLMs to maintain. More opportunity for hallucination, larger context windows needed, more tokens bought and spent for smaller and smaller code changes. I think the more code an LLM writes for an app, the worse that codebase becomes.

Sure but there’s no way Enron was the last Enron. Also keep in mind something can be “legal” and still misleading to shareholders. I’m sure there are loopholes, some of which may have been reintroduced by Republicans in the last 20 years.

(P/E ratios aren’t expressed in dollars)

So a PE of 500 means it would take 500 years for the earnings of the company to equal the current market cap (price per share X number of shares). This implies absurd (almost certainly impossible) growth over the next 500 years. Of course anyone expecting to pull their investment out and spend it on retirement can’t be looking at a 500-year investment horizon. I suppose the 1% can, though. What the hell else are they going to spend their cash on?

I think most people just vote for whoever’s mailer they like more. Where I live, the Republican candidate just sends out a mailer with him standing next to a firefighter and a cop and that’s really it. Maybe some garb about reducing crime and taxes at the same time. That’s enough for many (most?) to decide whether that’s the candidate they want to vote for.

This brings up a good point- can we just have Claude Code create a ripoff of this site and run it locally for free? Probably.

I think it's worth noting that the "work" these LLMs are doing is digital. ChatGPT cannot plow fields, or construct buildings and roads, or otherwise interact with the physical world. LLMs are useful because humanity has evolved (devolved?) into so much intangible work- documents, code, powerpoint presentations... One solar flare and that's all wiped out, and then we can say definitively that LLMs were a waste of time and energy.

Anecdotally I am seeing websites/web portals/et al. are much buggier. I have no doubt that software quality has declined since ChatGPT was made public, and tools like Claude Code are accelerating that "enshittification" of the world's codebase.

I think we should investigate the backgrounds of those making claims one way or another and rely on those backgrounds for determining credibility. I suspect that we'd find that those who are saying LLMs write great, bulletproof code with "100% unit test coverage" (true story- a coworker was bragging about 100% unit test coverage) are not really qualified to be software engineers. This is a trend I have noticed in my org. Those drinking the most LLM kool aid do NOT have an engineering/comp sci degree, have relatively little experience, resumes are incredibly weak (e.g., generic stuff that we've all done as software engineers).

We no longer have the luxury of welcoming bootcamp engineers into our field with open arms. We need to protect our craft. Call these fools out or they'll keep spreading hype/FOMO.

The unit test example has been my team's experience as well. The unit tests look good on the surface, but their passing or failing has little predictive value on whether there are actually bugs in the code.

Some people have suggested you write the unit tests by hand to basically "check" the LLM's work and keep it honest, but to write good unit tests you have to understand the underlying code, which takes time (since you didn't write it), so to me this is another bullet point that suggests LLMs will eventually be relegated to "StackOverflow+" duty - give me snippets, but I'll still write effectively all the code.

I’ve been a software engineer for over a decade. At what point is my “residency” over? At what point does the “community” decide I can write software with some level of competency and don’t need to solve fizz buzz over and over again?

I think of housing as a better example- KB Home cookie cutter special for $400k or 100-year-old craftsman house for $800k. Most are okay with the $400k cookie cutter special that probably has piss in the walls, because $800k is such a huge increase. I fear most companies won’t want to pay the extra cost for “the same software” to be written by hand.

Even though the synthetic benchmarks paint a picture of LLMs coming a long way since 2022, my practical experience has been that they aren’t tangibly better. No doubt someone reading this will chime in and say LLMs are way better at writing code or whatever, and maybe that’s true, but there’s no difference between ChatGPT 3.5 and Claude Opus 4.8 as far as my trusting the output. Opus 4.8 still messes up plenty. It’s particularly bad with identifying and fixing CI yaml, but it struggles in the usual areas too.

So I’m thinking we’ve just about reached apex with LLMs, and they have failed at replacing software engineers (companies can freeze hiring juniors at their own, future peril using any excuse they like).

LLMs seem to be best at writing web apps intended to be internal tools. I don’t need to ever really read the code because the functionality is relatively simple- but still valuable. I had Claude Code build me a CLI tool for running common kubectl commands against our EKS cluster. With EKS (AWS) you generally use the AWS CLI to authenticate and then choose the correct EKS cluster and then you can run your kubectl commands. So this CLI tool remembers all the AWS accounts and EKS cluster names and namespaces and pods that I care about. It’s been a huge time saver, and I never would have had the time to build it as part of my day to day. And because it’s an internal tool, I don’t have to worry about things like security/authentication, and any bugs aren’t as big a deal as if they were customer-facing. I wouldn’t want to use it for generating an app that is going on the public internet and doing anything sensitive/important/valuable with my or other people’s data (I’m sure that doesn’t stop others from vibe coding commercial products).

LLMs have taken much of the enjoyment out of coding for me, but I don’t think it has to be that way. I hope that as an industry we settle on LLMs being more like tools than human assistants. I think most of the engineers at my company are using LLMs as human assistants- most of them have agentic workflows set up and have premium subscriptions to Claude AND Gemini AND ChatGPT. Many have local LLMs running on their company MacBook Pros, but they can never manage to describe to me in plain English what those local LLMs are doing. I would compare local LLMs to Raspberry Pi clusters. They’re neat, and technically they can do stuff but they are incredibly impractical. I want so desperately to have the power of Claude Opus running locally, but we are incredibly, ridiculously, extremely far off (benchmarks are often misleading and nobody likes to talk about the paltry tokens/s they’re getting on their home rig).

So the problem for me is that I’ve noticed a trend with my coworkers- they usually don’t have a formal CS background (e.g., Comp Sci degree), their resumes are unimpressive, and they have personal websites where anyone can download a copy of their resume. I haven’t seen this at any previous job. It’s just weird, man. Anyway, so my problem is when they are purported to be the best of the best at this company, and they’re chugging the LLM kool-aid, I’m struggling to figure out whether they’re really ahead of the curve and getting the drop on the rest of us, or they’re doing basically no work and waving through each other’s PRs with little to no oversight on what the LLMs are writing. The bush league nature of many of the bugs I’m seeing going all the way to prod makes me think it’s the latter.

Meanwhile, these guys and their teams are completing an absurd number of story points each sprint. My team is dead last (and coincidentally we’re all pretty anti-AI for writing code).

I’m waiting for the other shoe to drop. I’m waiting for the C suite to call our project a failure and lay everyone off. The product generally works, though I think if the devs were truly rockstars, things would be much smoother. It seems like LLMs are letting middle-of-the-road or mediocre devs appear to be rockstars purely on output speed. Nobody in this org cares if the output has 4x the bugs it should have, just that it was completed quickly.

The main issue I think is that nobody- including the rockstar tech leads- knows how the system works. They don’t know the code. There’s a bug and it takes forever to track down. Don’t ask about unit tests.

I think in a perfect world, these LLMs are treated like another tool rather than another person- meaning you and I still write a large portion of the code, and we read and understand every line- especially the lines generated by LLMs. It’s still a huge productivity multiplier for me to not have to remember how to do mundane things like write out arrays into CSV files- before I’d find a close implementation on StackOverflow and tweak to my needs. Now I can just describe what I want the CSV to look like and Claude Code generally gets it right on the first try.

Enlighten us to these drawbacks. On the surface I am inclined to say the pros would outweigh the cons. Compared to other professions, software engineering seems to struggle the most with H-1B/Green Card abuse and interview processes. Job interviews are absurdly different (easier) for doctors, lawyers, et al. than for software engineers, and that I believe is because of the licensure. I do think licensure adds overhead to an industry (e.g., malpractice insurance, governing bodies, license management) and that probably discourages anyone with real power (like FAANG) to pursue it and try to set it as an industry-wide standard. Most software engineers in the U.S. are making around $130-140k, but lawyers and medical doctors usually make significantly more (perhaps because of the licensure overhead - I'm not sure if malpractice insurance is included in a medical doctor's salary- I would imagine it's not and is taken out of each paycheck like any other industry's health insurance benefits).